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@ -16,6 +16,7 @@ conda install pytorch torchvision cuda100 -c pytorch
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Searching CNNs
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Searching CNNs
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```
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```
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bash ./scripts-cnn/search.sh 1 base cifar10
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bash ./scripts-cnn/search.sh 1 base cifar10
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bash ./scripts-cnn/DMS-V-TrainV3.sh 1
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bash ./scripts-cnn/search-acc-v2.sh 3 acc2
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bash ./scripts-cnn/search-acc-v2.sh 3 acc2
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```
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```
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@ -219,8 +219,8 @@ def main():
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print_log('Finish with training time = {:}'.format( convert_secs2time(total_train_time, True) ), log)
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print_log('Finish with training time = {:}'.format( convert_secs2time(total_train_time, True) ), log)
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# clear GPU cache
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# clear GPU cache
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torch.cuda.empty_cache()
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#torch.cuda.empty_cache()
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main_procedure(config, args.dataset, args.data_path, args, basemodel.genotype(), 36, 20, log)
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#main_procedure(config, args.dataset, args.data_path, args, basemodel.genotype(), 36, 20, log)
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log.close()
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log.close()
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@ -1,7 +1,8 @@
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#!/usr/bin/env sh
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#!/usr/bin/env sh
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if [ "$#" -ne 2 ] ;then
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# bash scripts-cnn/DMS-V-TrainV3.sh 1
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if [ "$#" -ne 1 ] ;then
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echo "Input illegal number of parameters " $#
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echo "Input illegal number of parameters " $#
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echo "Need 2 parameters for the GPUs and the epochs"
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echo "Need 1 parameters for the GPUs and the epochs"
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exit 1
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exit 1
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fi
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fi
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if [ "$TORCH_HOME" = "" ]; then
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if [ "$TORCH_HOME" = "" ]; then
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@ -15,10 +16,10 @@ gpus=$1
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arch=acc2
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arch=acc2
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cutout=0
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cutout=0
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dataset=cifar10
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dataset=cifar10
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epoch=$2
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epoch=200
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SAVED=./snapshots/NAS/ACC-V3-Search-${arch}-${dataset}-cut${cutout}-${epoch}-E600
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SAVED=./snapshots/NAS/ACC-V3-Search-${arch}-${dataset}-cut${cutout}-${epoch}-E600
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CUDA_VISIBLE_DEVICES=${gpus} python ./exps-nas/acc_search_v3.py \
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CUDA_VISIBLE_DEVICES=${gpus} python ./exps-cnn/GDAS-Search.py \
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--data_path $TORCH_HOME/cifar.python \
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--data_path $TORCH_HOME/cifar.python \
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--arch ${arch} --dataset ${dataset} --batch_size 128 \
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--arch ${arch} --dataset ${dataset} --batch_size 128 \
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--save_path ${SAVED} \
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--save_path ${SAVED} \
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@ -26,5 +27,5 @@ CUDA_VISIBLE_DEVICES=${gpus} python ./exps-nas/acc_search_v3.py \
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--epochs ${epoch} --cutout ${cutout} --validate --grad_clip 5 \
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--epochs ${epoch} --cutout ${cutout} --validate --grad_clip 5 \
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--init_channels 16 --layers 8 \
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--init_channels 16 --layers 8 \
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--tau_max 10 --tau_min 1 \
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--tau_max 10 --tau_min 1 \
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--model_config ./configs/nas-cifar-cos.config \
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--model_config ./configs/nas-cifar-cos-cut.config \
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--print_freq 100 --workers 8
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--print_freq 100 --workers 10
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@ -18,12 +18,12 @@ dataset=cifar10
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epoch=200
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epoch=200
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SAVED=./snapshots/NAS/ACC-V2-Search-${arch}-${dataset}-cut${cutout}-${epoch}-E600
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SAVED=./snapshots/NAS/ACC-V2-Search-${arch}-${dataset}-cut${cutout}-${epoch}-E600
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CUDA_VISIBLE_DEVICES=${gpus} python ./exps-cnn/DARTS-Search.py \
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CUDA_VISIBLE_DEVICES=${gpus} python ./exps-cnn/GDAS-Search.py \
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--data_path $TORCH_HOME/cifar.python \
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--data_path $TORCH_HOME/cifar.python \
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--arch ${arch} --dataset ${dataset} --batch_size 128 \
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--arch ${arch} --dataset ${dataset} --batch_size 128 \
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--save_path ${SAVED} \
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--save_path ${SAVED} \
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--learning_rate_max 0.025 --learning_rate_min 0.001 --momentum 0.9 --weight_decay 0.0003 \
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--learning_rate_max 0.025 --learning_rate_min 0.001 --momentum 0.9 --weight_decay 0.0003 \
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--epochs ${epoch} --cutout ${cutout} --validate --grad_clip 5 \
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--epochs ${epoch} --cutout ${cutout} --validate --grad_clip 5 \
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--init_channels 16 --layers 8 \
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--init_channels 16 --layers 8 \
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--model_config ./configs/nas-cifar-cos.config \
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--model_config ./configs/nas-cifar-cos-cut.config \
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--print_freq 100 --workers 10
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--print_freq 100 --workers 10
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